While the complexity of deep learning models allows them to outperform humans on a growing number of tasks, it often renders them uninterpretable to human users. This limits the use of these methods in applications where decisions can have significant consequences, such as for instance in the fields of health care, finance or autonomous driving.
Uncertainty estimation (UE) methods aim to improve model interpretability, by associating an estimate of its uncertainty to each output [1]}.
As such, uncertainty estimates are expected to be high in value whenever we give as input a sample that is out of the distribution (OOD) of the dataset that the model was trained with.
